Search Results (802)

This case study presents a comparative analysis of real-world operation of two residential domestic hot water (DHW) preparation methods both connected to their own air-to-water heat pump (HP) located in Central Europe. One system employs a conventional configuration with separate tanks and an internal heating coil (HP-B), while the other features a compact tank-in-tank setup where DHW is heated via an integrated buffer tank (HP-A). Both systems were monitored under real operational conditions, with seasonal and annual coefficients of performance (COP, SCOP) calculated to evaluate efficiency. In the absence of complete thermal output data for one system, a reconstruction method based on the other’s performance and known heat losses was applied. The findings confirm that DHW system design significantly affects seasonal efficiency, particularly during summer operation when heating DHW dominates the energy load. The energy cost savings on heating during summer months could reach 44%. The tank-in-tank system showed higher electrical consumption and lower SCOP due to internal heat transfer dynamics and dual-function operation. The study further shows associated energy and cost differences and demonstrates a practical approach to comparing real-world systems offering insights for design optimisation and operational strategy. The authors of the article used the results of their research and experience from implementations as very effective feedback for further research and development. The novelty and uniqueness of the article lie in the energy comparison of two different connections of the hot water and heating water storage tanks with heat source systems using an “air-to-water” heat pump. The benefit of the solution in question is evident from the technical and economic evaluation. Full article

While walking robots possess significantpotential for various real-world applications, the reliance on high-performance sensors and complex control architectures for precise gait control remains a significant barrier to commercialization and lightweight design. To overcome these engineering limitations and lay the groundwork for a sensing paradigm adaptable to complex terrains, this study proposes an AI-based sensorless feedback control framework that incorporates the biological principles of proprioception. To this end, a walking robot leveraging the morphological intelligence of the Klann linkage was developed. We constructed a time-series dataset by defining motor current signals as ‘interoceptive sensing’ information—analogous to biological muscle feedback—and synchronizing them with absolute angular data. This dataset was used to train an Attention-LSTM (A-LSTM) model, which predicts future motor states in real-time by decoding nonlinear physical information embedded within internal current data, independent of external environmental sensors. By integrating the proposed model into a PI controller, a stable biomimetic walking loop was successfully implemented without the need for additional position sensors. Full article

Against the backdrop of sustainable development and global climate governance, this study focuses on the evaluation and trend prediction of provincial carbon emission efficiency and constructs a multi-model integrated analytical framework featuring “data preprocessing—efficiency decomposition—dynamic forecasting—policy deduction”. First, economic, energy consumption and carbon emission data for 30 provinces in China from 2009 to 2019 are collected. Data cleaning is performed through outlier identification and Lagrange interpolation, and a cross-regionally comparable quantification system is established based on a unified carbon emission standard, laying a foundation for subsequent analysis. Second, data envelopment analysis (DEA) is adopted to decompose carbon emission efficiency. It is found that approximately 23% of provinces lie on the technical efficiency frontier, with the average variance share of technical inefficiency being 0.62; 6% of provinces have the potential for scale expansion; and 10% suffer from diseconomies of scale, reflecting significant structural efficiency losses in regions concentrated with high-carbon industries. Third, the long short-term memory (LSTM) neural network is employed for dynamic forecasting and scenario simulation of carbon emissions by 2025. The model’s prediction error in 2019 is controlled within 8.7%. Simulation results show that when the share of clean energy rises to 35%, China’s national carbon emission growth rate can be reduced to 1.2% by 2025. However, multi-scenario sensitivity analysis indicates that the achievement of this target highly depends on policy enforcement intensity and power grid accommodation capacity. In addition, stochastic frontier analysis (SFA) reveals the heterogeneous contributions of different energy types to economic and social outputs. The consumption elasticities of electricity, liquefied petroleum gas and gasoline are significantly positive, whereas the negative elasticities of oil, fuel oil and coal deeply reflect the low energy utilization efficiency and rigid lock-in of high-carbon industries in some regions. Finally, combined with efficiency evaluation, trend prediction and mechanism analysis, differentiated emission reduction strategies are proposed for technologically backward provinces, scale-imbalanced provinces and clean energy base provinces, forming a complete closed loop from “efficiency diagnosis” to “future deduction” and then to “policy feedback”. This study breaks through the limitations of a single model. Through the coupling of parametric and non-parametric methods, as well as the integration of dynamic forecasting and scenario simulation, it effectively addresses issues such as data heterogeneity. It provides scientific support for local governments to formulate emission reduction policies and optimize energy structures, establishes a methodological foundation for industrial efficiency analysis and international carbon responsibility allocation research, and helps to promote regional clean, low-carbon, and sustainable development. Full article

A capsule endoscope (CE) provides noninvasive access to the gastrointestinal tract, offering diagnostic information that cannot be obtained through external imaging alone. However, during the examination inside the stomach, the CE’s posture may change rapidly as it moves within a dynamically deforming organ, making it difficult to determine its orientation using only the onboard camera feedback. To address this problem, this study proposes a method that employs an external array of Hall Effect Sensors (HES) to estimate the capsule’s position and orientation in real time, based on the magnetic field generated by a permanent magnet (PM) embedded inside the capsule, without the need for any additional internal sensors. This approach introduces a unified magnetic actuation and localization framework that enables real-time 5-degree-of-freedom posture estimation using only the internal PM of the capsule. Furthermore, the proposed system features an integrated architecture capable of simultaneous actuation and localization. To enhance system practicality, the sensor module and communication board were combined into a single unit that employs a digital serial communication scheme, eliminating the need for analog to digital conversion of sensing signals. By avoiding additional onboard sensors and employing a PM-based actuation system, the proposed system simplifies hardware configuration by preserving capsule miniaturization and by eliminating the high power consumption and thermal issues associated with electromagnet-based actuation, while maintaining accurate real-time tracking performance. Through an optimization process, the system achieved a position error of less than 2 mm and an angular error within 2° over a sensing range of up to 60 mm. Repeated experiments further validated the system’s effectiveness and reliability under realistic operating conditions, demonstrating its feasibility for compact and clinically applicable active capsule endoscopy systems. Full article

The rapid advancement of automated essay scoring (AES) has been constrained by a representation bottleneck, where monolithic models collapse diverse facets of writing constructs into a single, uninterpretable signal, undermining the pedagogical value of multi-dimensional rating traits. To address this limitation, the RoBERTa-based Trait-Aware Transformer (RoBERTa-TAT) is introduced. This architectural reframing replaces unified pooling with parallel, trait-specific attention streams, preserving and disentangling critical features such as conceptual depth and mechanical precision. Tested on the ASAP Dataset-7, RoBERTa-TAT attains a new state-of-the-art Quadratic Weighted Kappa (QWK) of 0.936, outperforming sequential baselines and conventional Transformer variants. Beyond gains in accuracy, this trait-specialized architecture recasts scoring from a black-box prediction into a transparent diagnostic tool, enabling actionable, fine-grained feedback at different rating traits. High-resolution inspection reveals that the model’s internal representations correlate with specific linguistic markers—such as discourse connectives for organization—suggesting a degree of structural alignment with expert judgment. By aligning high-capacity representation learning with the granular demands of formative assessment, RoBERTa-TAT provides a practical, interpretable blueprint for deploying accountable AI in education and broadening access to expert diagnostic insight. Full article

Despite the pervasive role of digital platforms in contemporary higher education, existing measurement tools fail to capture students’ psychological dependence on online approval within academic contexts, focusing instead on technical competencies or clinical addiction symptoms. This study developed and psychometrically validated the Digital Validation Seeking Scale (DVSS), a multidimensional instrument measuring university students’ reliance on digital feedback for academic and identity confirmation. Two independent samples of Egyptian undergraduate students were recruited: an exploratory sample of 511 students and a confirmatory sample of 740 students from six universities. The DVSS underwent rigorous content validation by eleven experts, exploratory factor analysis (EFA) using Principal Axis Factoring with Promax rotation, and confirmatory factor analysis (CFA) comparing competing structural models. Results revealed a robust four-factor structure comprising Academic Self-Quantification (ASQ), Feedback Hyper-vigilance (FHV), Social Comparison (SC), and Performative Studiousness (PS), with the first-order four-factor model demonstrating superior fit indices. The final 19-item scale exhibited excellent internal consistency, with Cronbach’s alpha coefficients ranging from 0.807 to 0.938 for subscales and total score, respectively, and strong test–retest reliability. The DVSS provides researchers and practitioners with a theoretically grounded, psychometrically sound instrument for identifying maladaptive digital validation patterns before they compromise academic engagement or psychological well-being, enabling targeted interventions within hybrid educational environments. Full article

Background/Objectives: Traditional demonstrations are a common way to teach clinical skills, but they often feel unstructured and inconsistent. Peyton’s four-step approach provides a more organized, student-focused method that might help learners pick up skills better. This study compared the standard demonstration method with Peyton’s approach for teaching ENT procedures to interns. Methods: A prospective study was conducted at a single center with two groups: Group A received a conventional single-pass demonstration. Group B was taught using Peyton’s structured four-step approach (silent demonstration, deconstruction, verbal comprehension, and performed verbalization). Both groups were trained on three ENT skills—anterior rhinoscopy, Trotter’s method, and anterior nasal packing—then tested using OSCE checklists. We also asked students for their feedback through a simple questionnaire. Results: For anterior rhinoscopy, both groups performed similarly. But students taught with Peyton’s method did significantly better on Trotter’s method and nasal packing (p = 0.0098 and 0.004). Overall, they preferred Peyton’s approach, remembered the steps better, and wanted to use it for future training (p < 0.005). Conclusions: While traditional demonstrations are straightforward, Peyton’s structured, hands-on four-step method leads to better skill learning and retention for medical students. Full article

This paper addresses voltage fluctuation issues in distribution networks under high penetration of renewable energy. It proposes a collaborative voltage regulation method for multi-microgrid systems considering operational economy. To mitigate voltage violations and fluctuations caused by intermittent distributed generation such as photovoltaics, this paper develops a bi-level coordinated optimization framework with bidirectional feedback. At the upper level, the distribution network acts as the global regulator, suppressing voltage fluctuations by optimizing the active power output of microgrids while dynamically issuing voltage constraints and power exchange boundaries. At the lower level, each microgrid serves as a local response agent. While complying with the regulation requirements from the upper level, it coordinates its internal distributed resources, including PV, energy storage, and electric vehicles, and optimizes electricity market purchases to minimize its own operating cost. The framework moves beyond traditional one-way command models, achieving bidirectional coordination between global optimization and local autonomy. Simulations based on a modified IEEE 33-bus system show that the proposed method maintains all node voltages within the allowable range, significantly reduces voltage fluctuations, and lowers the total electricity purchase cost of the microgrids by approximately 11%, thereby enhancing both voltage stability and economic efficiency of the system. Full article

Laparoscopic surgery provides minimally invasive access to the abdominal cavity but poses control challenges for robotic systems due to the fulcrum constraint at the abdominal wall and the simultaneous interaction of the instrument with both the abdominal wall and internal soft tissue. While current clinical platforms (e.g., da Vinci) primarily rely on visual feedback and do not possess force sensors at the instrument tip, the transition to autonomous robotic surgery requires precise force feedback to ensure safety and effective tissue manipulation. Therefore, developing methods to decouple interaction forces using a single force sensor configuration is a critical enabling technology for future instrumented surgical robots. This paper presents a force-decoupling method that estimates, using only one force sensor, the individual forces applied to the abdominal wall and to internal soft tissue through a viscoelastic modeling approach based on Maxwell elements. Two configurations were evaluated, showing that a single-element Maxwell model provides the best trade-off between accuracy and computational complexity, achieving estimation errors of 9% and 13% for abdominal wall forces, with a root mean square error (RMSE) below 0.36 N. The method was implemented and experimentally validated in a force-controlled robotic system, demonstrating its effectiveness in improving force regulation and interaction safety without requiring additional sensors. Full article

Deep neural networks (DNNs) have achieved remarkable success in computer vision yet remain vulnerable to adversarial examples. Existing attacks typically distribute perturbations uniformly across the input, without leveraging the model’s internal attention mechanism, and fail to adapt to model responses. To tackle these limitations, we propose AG² (Attention-Guided Adversarial Sample Generation), an adversarial attack algorithm that uses dynamically updated attention maps to guide perturbation placement and a dynamic feedback mechanism for adaptive optimization. AG² comprises three steps: feature extraction and attention-weight computation, iterative optimization of perturbations guided by attention maps, and adjustment of optimization parameters based on attention shifts. By concentrating perturbations in regions receiving high attention from the victim model, AG² improves attack effectiveness while preserving visual imperceptibility. The dynamic feedback mechanism further maintains robustness against defended models such as those trained with defensive distillation. Experiments on MNIST, CIFAR-10, and ImageNet show that AG² achieves attack success rates of 93.7%, 93.5%, and 85.0%, respectively, outperforming prior methods. Moreover, AG² exhibits strong cross-architecture transferability, achieving a 69.5% success rate on Vision Transformers, which is higher than the previous method’s 55.3% by 14.2%. Theoretical analysis provides convergence guarantees and stability bounds for the proposed attention-guided optimization. Full article

Improper internal states in proton exchange membrane fuel cells (PEMFCs), such as insufficient reactant concentration, lower membrane water content, and excessive liquid water, will lead to significant reductions in durability and reliability, which is a bottleneck restricting the large-scale commercial application of the PEMFC system. Closed-loop management with internal state feedback is regarded as a promising strategy for prolonging its lifespan and enhancing its reliability. The key issue for the closed-loop management strategy is how to estimate the internal operating state of the PEMFC stack accurately and quickly. Consequently, an estimation method of stack internal operating states based on the medium frequency impedance phase angle measurement, which has the characteristics of short acquisition time, small measurement error, and high resolution, is proposed in this paper. The sensitivity, monotonicity, correlation analysis in the steady state, and response characteristics analysis in the dynamic state show that the proposed method is effective, competent, and qualified for internal state estimation. Then, the estimated internal state is applied to the system’s closed-loop management as feedback. The experiment results show that the PEMFC can be maintained at the expected state and that improper states will be avoided. The proposed estimation technology will significantly facilitate the system’s closed-loop management, thereby enhancing the reliability and durability of PEMFCs. Full article

We report the development of a high-power, cost-effective, and rapidly tunable laser system optimized for rubidium optical pumping in spin-exchange optical pumping (SEOP) applications. The system combines a spectrally narrowed diode laser bar with a low-cost yet high-stability thermal-management architecture based on consumer-grade CPU liquid-cooling components. Wavelength narrowing and fast tuning are achieved by linearly translating a chirped volume Bragg grating (CVBG), providing mode-hop-free, continuous wavelength control without relying on slow thermal tuning mechanisms. Long-term wavelength stability is ensured through a terminal proportional–integral–derivative (PID) feedback loop that locks the laser directly to the rubidium absorption spectrum in the pumping cell, rather than to an internal reference. Operating near 795 nm, the laser delivers up to 40 W of optical power with a measured linewidth of approximately 0.15 nm. The system supports rapid wavelength agility over a continuous tuning range of $794.73\pm 0.24$ nm and exhibits stable spectral performance during extended operation. Owing to its compact design, fast response, and substantially lower cost than conventional volume-grating-based systems, this laser architecture provides a practical and scalable solution for SEOP and other precision atomic and spectroscopic applications that require high power, a narrow linewidth, and robust wavelength stability. Full article

Background/Objectives: Aged care residents are highly vulnerable to vaccine-preventable diseases. Despite recommendations and funding under Australian programs, vaccination rates among residents for COVID-19, influenza, pneumococcal and shingles remain sub-optimal. The aim of this work was to assess if tailored quality improvement interventions would improve vaccination coverage in aged care residents. Methods: This was a quality improvement initiative evaluated using a quasi-experimental pre–post design. Building on previously identified barriers and enablers, a package of interventions and resources was developed to support consent processes, vaccination planning, and tracking. Pre- and post-intervention vaccination coverage was assessed using resident lists from participating aged care facilities and data extracted from the Australian Immunisation Register (AIR) at two time points, 14 months apart. A process evaluation survey was distributed to RACF staff. Results: Of the 6964 residents listed, 5153 (74%) remained registered in AIR when data was extracted post-intervention. Shingles showed the greatest improvement in absolute difference (+23.4%), followed by pneumococcal (+14.2%) and influenza (+10.9%), despite a high baseline of 68.5%. COVID-19 coverage declined by 7.4% when applying a 6-month reporting interval. Twenty-five staff completed the process evaluation survey; 45% of respondents identified discrepancies between AIR data and internal records, indicating underreporting by external providers. Interventions including the consent template and vaccination tracker were reported as useful and were used to support local vaccination. Conclusions: This quality improvement initiative improved coverage for three of the four recommended and funded vaccines for RACF residents and demonstrated the value of tailored interventions informed by consumer and provider feedback. The approach potentially offers a scalable model for improving vaccination rates in aged care across Australia. Full article

Farmland quantity continues to decline, land abandonment is a serious concern, and local quality degradation remains unresolved. This situation, in which large-scale farmland abandonment continues, is likely to induce a series of food security and ecological protection problems. However, strengthening the protection and development of abandoned farmland (AF) is very difficult. In response to this issue, this paper provides a comprehensive review and synthesis of domestic and international research on AF. The results show that the prior research has largely focused on information acquisition and the analysis of driving factors, while relatively limited attention has been given to pathways for the reuse and management of AF, with few relevant studies and practical examples available. In addition, no clear theoretical framework has been developed to evaluate and manage the multiple elements of and the overall process leading to AF. Building on an examination of the feasibility of applying resilience theory to the management of AF, this paper defines the conceptual scope and core meaning of AF resilience management and constructs a resilience management implementation path based on the steps of objective determination, problem profiling, evaluation feedback, and scheme formulation. This framework helps reveal the structure–process–function evolutionary characteristics of AF across different development stages and provides analytical support for the design of differentiated and adaptable management strategies. Full article

Financial systems are quintessential complex adaptive systems, where stability emerges from the dynamic interactions among multiple subsystems and regulatory components. Grounded in systems theory, this study re-frames the establishment of China’s fintech regulatory sandbox as a systemic intervention within the broader financial governance framework. Utilizing this policy as a quasi-natural experiment, we employ a difference-in-differences (DID) model integrated with spatial econometric modeling to evaluate its impact on regional financial system risk—an emergent property of the system. The benchmark regression results indicate that this systemic policy innovation significantly enhances regional financial resilience, with effects that are both continuous and robust. Mechanism tests, analyzed through the lens of subsystem coordination, demonstrate that the policy curbs systemic risk by improving the synergy within economic inner cycles, outer cycles, and their dual-cycle integration, thereby optimizing the system’s internal structure and feedback loops. Further analysis reveals a significant negative spatial spillover effect, evidencing the policy’s role in reshaping inter-regional systemic linkages: it reduces financial risk in both implementing and neighboring regions, with the effect’s intensity following an inverted U-shaped pattern relative to distance. Heterogeneity analysis shows that the policy’s inhibitory effect varies significantly across different systemic configurations, including risk circulation patterns, macro–micro risk perspectives, financial inclusion coverage, government–market relationships, and the north–south regional divide. These findings provide critical insights for developing synergistic macro-prudential and micro-behavioral regulatory mechanisms, contributing to a more robust and adaptive financial security framework from a systems governance perspective. Full article